MethylSeg

MethylSeg is a Python toolkit for identifying methylation domains from whole-genome bisulfite sequencing (WGBS) and microarray methylation data. It supports data preparation, methylation-state model training, genome segmentation, region cleaning, and visualization.

This introduces the standard workflow. For the complete API reference and example notebooks, see the MethylSeg documentation.

Important

Microarray support has currently been tested with Illumina HumanMethylation450 BeadChip (HM450K) data. Other methylation microarrays, such as Illumina HumanMethylation27 and MethylationEPIC, can be used, but array-specific defaults are not yet available and these platforms have not been formally tested.

Hardware requirements

Memory usage depends on the input data type and dataset size. The following values were observed during testing:

Data type	Typical memory usage	Recommended system memory
WGBS	~12 GB average; up to ~15 GB peak	≥16 GB
HM450K	<1 GB	≥8 GB

Note

Numba has known compatibility issues on ARM-based systems. See the Troubleshooting guide for installation guidance.

Installation

Install from TestPyPI

python -m pip install \
--index-url https://test.pypi.org/simple/ \
--extra-index-url https://pypi.org/simple/ \
methylseg

or install the current version from GitHub:

python -m pip install "git+https://github.com/clementlab/MethylSeg.git"

Reference files

Sample data

Three reference samples are available for use in tutorials and testing.

methylseg download_data_files

The two colon cancer samples were originally generated by Hansen et al. (2011) and were obtained from the data distributed with MethyLasso (Balaramane et al., 2024).

The liver cancer sample, TCGA-BD-A3EP-01A, was obtained from the TCGA-LIHC project through the NCI Genomic Data Commons.

Sample name	WGBS filename	HM450K filename
WGBS_colon-primary-tumor_1	WGBS_colon-primary-tumor_1_wgbs.tsv	WGBS_colon-primary-tumor_1_450k.beta
WGBS_colon-primary-tumor_2	WGBS_colon-primary-tumor_2_wgbs.tsv	WGBS_colon-primary-tumor_2_450k.beta
TCGA-BD-A3EP-01A	N/A	TCGA-BD-A3EP-01A_450k.tsv

References

1. Hansen, K. D., et al. Increased methylation variation in epigenetic domains across cancer types. Nature Genetics43, 768–775 (2011).
2. Balaramane, D., Spill, Y. G., Weber, M. & Bardet, A. F. MethyLasso: a segmentation approach to analyze DNA methylation patterns and identify differentially methylated regions from whole-genome datasets. Nucleic Acids Research52, e98 (2024).

Pretrained models

Two pretrained models are provided for tutorials, testing, and exploratory analysis:

Model filename	Training data
wgbs_colon_model	WGBS_colon-primary-tumor_1_wgbs.tsv
tcga_hm450k_model	TCGA-BD-A3EP-01A_450k.tsv

Load a pretrained model with get_pretrained_model:

# WGBS modelwgbs_saved=MethylSegPathway.get_pretrained_model(
out_dir=OUTPUT_DIR,
resolution="wgbs",
)
# HM450K modelhm450k_saved=MethylSegPathway.get_pretrained_model(
out_dir=OUTPUT_DIR,
resolution="450k",
)

Quickstart

The default workflow trains a model on your input, segments the sample, cleans the calls, and draws a cleaned methylation state overlay.

Use resolution="wgbs" for WGBS count tables or resolution="450k" for HM450K beta-value tables. Other microarray platforms may require manually configured parameters until array-specific defaults are added.

frompathlibimportPathfrommethylsegimportMethylSegPathway, MethylationStatesfrommethylseg.helper_classesimportDATA_DIRreference_dir=DATA_DIR/"reference_files"# WGBS: replace these with your own sample name and count table.# sample_name = "WGBS_colon-primary-tumor_1"# sample_file = reference_dir / "WGBS_colon-primary-tumor_1_wgbs.tsv.gz"# resolution = "wgbs"# TCGA/HM450K alternative:sample_name="TCGA-BD-A3EP-01A"sample_file=reference_dir/"TCGA-BD-A3EP-01A_450k.tsv.gz"resolution="450k"sample_info, removed_df=MethylSegPathway.prepare_sample_info(
sample_name=sample_name,
sample_file=sample_file,
resolution=resolution,
remove_low_coverage_like_cpgs=True,
)
pathway=MethylSegPathway(
train_sample_info=sample_info,
out_dir=Path("out/quickstart") /sample_name,
)
pathway.run_pathway()
fig=pathway.plot_labels(
sample_info=sample_info,
sample_info_removed=removed_df,
chrom="chr1",
region_start=2_200_000,
region_end=3_700_000
)

run_pathway(sample_info=sample_info, chroms=["chr1"]) performs fitting, segmentation, cleaning, and summary-file writing in one call.

Input formats

MethylSeg accepts tab-delimited .tsv and .tsv.gz files containing DNA methylation measurements. Genomic coordinates must use the 0-based, half-open coordinate system, and chromosome names must be consistent throughout each file.

Two BED-like input formats are supported.

Beta-value format

This format can be used for either WGBS or microarray data. It contains four required columns:

1. chromosome;
2. CpG start position;
3. CpG end position; and
4. methylation beta value.

For microarray data, an optional fifth column containing the probe identifier may be included. Probe identifiers are retained as metadata.

CpG_chrm	CpG_beg	CpG_end	beta	probe
chr1	15864	15866	0.0	cg13869341
chr1	29406	29408	0.0	cg12045430
chr1	29424	29426	0.0	cg20826792

WGBS count format

This format contains five required columns:

1. chromosome;
2. CpG start position;
3. CpG end position;
4. methylated read count; and
5. total read coverage.

MethylSeg calculates the beta value internally as: $ \mathrm{beta} = \frac{\mathrm{methylated\ reads}}{\mathrm{total\ coverage}}. $

CpG_chrm	CpG_beg	CpG_end	meth	coverage
chr1	10468	10470	14	15
chr1	10470	10472	10	10
chr1	10483	10485	23	28

Outputs

The final output of MethylSeg consists primarily of BED files containing the identified methylation domains. Separate files are generated for each methylation state: HIGH, INTERMEDIATE, LOW, and PMD.

For each chromosome and state, MethylSeg writes:

Output	Filename pattern	Description
Raw regions	segments_{chrom}_{sample_id}_{resolution}_{state}.bed	Regions produced directly by genome segmentation
Cleaned regions	clean_regions/segments_cleaned_{chrom}_{sample_id}_{resolution}_{state}.bed	Regions retained after merging and filtering
Cleaned metadata	clean_regions/metadata_cleaned_{chrom}_{sample_id}_{resolution}_{state}.tsv	Additional information about the cleaned regions

After all requested chromosomes have been processed, MethylSeg also creates genome-wide files for each state under summary_files/:

Output	Filename pattern
Genome-wide raw regions	segments_raw_{state}.bed
Genome-wide cleaned regions	segments_cleaned_{state}.bed
Genome-wide cleaned metadata	metadata_cleaned_{state}.tsv

A simplified output directory has the following structure:

methylseg_output/
├── segments_chr1_sample.wgbs_PMD.bed
├── segments_chr1_sample.wgbs_LOW.bed
├── clean_regions/
│ ├── segments_cleaned_chr1_sample.wgbs_PMD.bed
│ ├── segments_cleaned_chr1_sample.wgbs_LOW.bed
│ ├── metadata_cleaned_chr1_sample.wgbs_PMD.tsv
│ └── metadata_cleaned_chr1_sample.wgbs_LOW.tsv
└── summary_files/
├── segments_raw_PMD.bed
├── segments_raw_LOW.bed
├── segments_cleaned_PMD.bed
├── segments_cleaned_LOW.bed
├── metadata_cleaned_PMD.tsv
└── metadata_cleaned_LOW.tsv

Each BED file contains 0-based, half-open genomic coordinates and the assigned methylation state. For example, segments_cleaned_PMD.bed may contain:

Chromosome	Start	End	State
chr1	1,261,344	1,323,691	PMD
chr1	2,789,167	2,999,312	PMD
chr1	3,885,138	3,916,529	PMD
chr1	6,167,866	6,186,177	PMD

The BED files themselves are tab-delimited and do not contain a header.

Citation

A manuscript describing MethylSeg is in preparation. Citation information will be added when it becomes available.

Planned support

• Add defaults for HM27 and EPIC microarray formats

Reporting issues

To report a bug, request a feature, or ask a question about MethylSeg, open an issue on the GitHub issue tracker.

When reporting a bug, please include:

• Your MethylSeg version
• Your Python version and operating system
• The complete error message or traceback
• A minimal example that reproduces the problem, when possible

License

MethylSeg is distributed under the BSD 3-Clause License.